paddlespeech.t2s.modules.transformer.lightconv module

Lightweight Convolution Module.

class paddlespeech.t2s.modules.transformer.lightconv.LightweightConvolution(wshare, n_feat, dropout_rate, kernel_size, use_kernel_mask=False, use_bias=False)

Bases: Layer

Lightweight Convolution layer.

This implementation is based on https://github.com/pytorch/fairseq/tree/master/fairseq

Args:

wshare (int):

the number of kernel of convolution

n_feat (int):

the number of features

dropout_rate (float):

dropout_rate

kernel_size (int):

kernel size (length)

use_kernel_mask (bool):

Use causal mask or not for convolution kernel

use_bias (bool):

Use bias term or not.

Methods

__call__(*inputs, **kwargs)	Call self as a function.
add_parameter(name, parameter)	Adds a Parameter instance.
add_sublayer(name, sublayer)	Adds a sub Layer instance.
apply(fn)	Applies fn recursively to every sublayer (as returned by .sublayers()) as well as self.
buffers([include_sublayers])	Returns a list of all buffers from current layer and its sub-layers.
children()	Returns an iterator over immediate children layers.
clear_gradients()	Clear the gradients of all parameters for this layer.
create_parameter(shape[, attr, dtype, ...])	Create parameters for this layer.
create_tensor([name, persistable, dtype])	Create Tensor for this layer.
create_variable([name, persistable, dtype])	Create Tensor for this layer.
eval()	Sets this Layer and all its sublayers to evaluation mode.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(query, key, value, mask)	Forward of 'Lightweight Convolution'.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
load_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
named_buffers([prefix, include_sublayers])	Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor.
named_children()	Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself.
named_parameters([prefix, include_sublayers])	Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter.
named_sublayers([prefix, include_self, ...])	Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer.
parameters([include_sublayers])	Returns a list of all Parameters from current layer and its sub-layers.
register_buffer(name, tensor[, persistable])	Registers a tensor as buffer into the layer.
register_forward_post_hook(hook)	Register a forward post-hook for Layer.
register_forward_pre_hook(hook)	Register a forward pre-hook for Layer.
set_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
set_state_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
state_dict([destination, include_sublayers, ...])	Get all parameters and persistable buffers of current layer and its sub-layers.
sublayers([include_self])	Returns a list of sub layers.
to([device, dtype, blocking])	Cast the parameters and buffers of Layer by the give device, dtype and blocking.
to_static_state_dict([destination, ...])	Get all parameters and buffers of current layer and its sub-layers.
train()	Sets this Layer and all its sublayers to training mode.

backward
register_state_dict_hook

forward(query, key, value, mask)

Forward of 'Lightweight Convolution'.

This function takes query, key and value but uses only query. This is just for compatibility with self-attention layer (attention.py)

Args:

query (Tensor):

input tensor. (batch, time1, d_model)

key (Tensor):

NOT USED. (batch, time2, d_model)

value (Tensor):

NOT USED. (batch, time2, d_model)

mask(Tensor):

(batch, time1, time2) mask

Return:

Tensor: ouput. (batch, time1, d_model)